Gabriel M. Guerra

Data-centric iteration in dynamic workflows

Dynamic workflows are scientific workflows to support computational science simulations, typically using dynamic processes based on runtime scientific data analyses. They require the ability of adapting the workflow, at runtime, based on user input and dynamic steering. Supporting data-centric iteration is an important step towards dynamic workflows because user interaction with workflows is iterative. However, current support for iteration in scientific workflows is static and does not allow for changing data at runtime. In this paper, we propose a solution based on algebraic operators and a dynamic execution model to enable workflow adaptation based on user input and dynamic steering. We introduce the concept of iteration lineage that makes provenance data management consistent with dynamic iterative workflow changes. Lineage enables scientists to interact with workflow data and configuration at runtime through an API that triggers steering. We evaluate our approach using a novel and real large-scale workflow for uncertainty quantification on a 640-core cluster. The results show impressive execution time savings from 2.5 to 24 days, compared to non-iterative workflow execution. We verify that the maximum overhead introduced by our iterative model is less than 5% of execution time. Also, our proposed steering algorithms are very efficient and run in less than 1 millisecond, in the worst-case scenario. Algebraic operators support data-centric iteration in dynamic workflows.Runtime data lineage, a concept inspired by provenance enables dynamic loops.Two algorithms support runtime adaptation of the workflow based on user input.Real-life experiment for Uncertainty Quantification in the Oil & Gas domain.A novel iterative workflow for Uncertainty Quantification is steered by users.

Uncertainty quantification in numerical simulation of particle-laden flows

Numerical models can help to push forward the knowledge about complex dynamic physical systems. Modern approaches employ detailed mathematical models, taking into consideration inherent uncertainties on input parameters (phenomenological parameters or boundary and initial conditions, among others). Particle-laden flows are complex physical systems found in nature, generated due to the (possible small) spatial variation on the fluid density promoted by the carried particles. They are one of the main mechanisms responsible for the deposition of sediments on the seabed. A detailed understanding of particle-laden flows, often referred to as turbidity currents, helps geologists to understand the mechanisms that give rise to reservoirs, strategic in oil exploration. Uncertainty quantification (UQ) provides a rational framework to assist in this task, by combining sophisticated computational models with a probabilistic perspective in order to deepen the knowledge about the physics of the problem and to access the reliability of the results obtained with numerical simulations. This work presents a stochastic analysis of sediment deposition resulting from a turbidity current considering uncertainties on the initial sediment concentrations and particles settling velocities. The statistical moments of the deposition mapping, like other important features of the currents, are approximated by a Sparse Grid Stochastic Collocation method that employ a parallel flow solver for the solution of the deterministic problems associated to the grid points. The whole procedure is supported and steered by a scientific workflow management engine designed for high performance computer applications.

Constructing Accurate Phenomenological Surrogate for Fluid Structure Interaction Models

The accurate prediction of structural instability caused by vortex shedding behind bodies or by nonlinear unsteady aerodynamic is fundamental to avoid the degradation of structural performance or even failure of the system. Numerous approaches can represent analytical models to modeling both the structure and fluid. The CFD (Computational Fluid Dynamics) approaches consists of solving the Navier-Stokes equations directly, mostly limited by heavily computational costs that, many times, are tough to satisfy in the practical engineering. To increase the expectations of solving practical problems, the use of phenomenological surrogate models, are an alternative approach for the underlying physics, where phenomenological equations emulate the fluid dynamic forces acting on the structure, have become an essential tool to simplify the analysis and can be a very useful tool in broad industrial applications. However, constructing accurate surrogate models introduce additional challenges that will be addressed in this work. Most of these models present a series of empirical parameters that need to be calibrated from experimental data. To build an accurate phenomenological model we need putting this parameter variability in the general context of Uncertainty Quantification (UQ). We present a phenomenological model for fluid-structure interaction to be calibrated. In the first stage of this processes, we do global sensitivity analysis for the empirical parameters of the model, where uncertainty source is introduced earlier using the Sparse Grid Stochastic Collocation method. After this, a backward parameter estimation analysis is done using a Bayesian technique to calibrate these empirical parameters, through exploring posterior density functions. Synthetic data were generated as reference simulating experimental data to show the calibration technique used. This kind of analysis can help to understand the effects of varying empirical parameters in the response variables. The influence of these parameters and other coefficients that affect the dynamical response is analyzed and also discussed.